(imgs, labels, size)
| 395 | return imgs, labels |
| 396 | |
| 397 | def data_padding_fixsize(imgs, labels, size): |
| 398 | for idx, img in enumerate(imgs): |
| 399 | label = labels[idx] |
| 400 | h, w = img.shape[:2] |
| 401 | h_padding = size[0] |
| 402 | w_padding = size[1] |
| 403 | |
| 404 | h_padding1 = math.ceil(h_padding) |
| 405 | h_padding2 = math.floor(h_padding) |
| 406 | |
| 407 | w_padding1 = math.ceil(w_padding) |
| 408 | w_padding2 = math.floor(w_padding) |
| 409 | |
| 410 | img = np.pad(img, ((h_padding1, h_padding2), (w_padding1, w_padding2), (0,0)), 'symmetric') |
| 411 | label = np.pad(label, ((h_padding1, h_padding2), (w_padding1, w_padding2)), 'constant') |
| 412 | imgs[idx] = img |
| 413 | labels[idx] = label |
| 414 | return imgs, labels |
| 415 | |
| 416 | def five_crop_mix(ims, labels, x_s, size, scale=8): |
| 417 | crop_imgs = [] |
nothing calls this directly
no outgoing calls
no test coverage detected